Semester course; 3 lecture hours. 3 credits. Enrollment is restricted to students with graduate standing or those with one course in statistics and permission of instructor. Develop quantitative skills for the visualization, manipulation, analysis, and communication of environmental ‘big data.’ This course focuses on spatial environmental data analysis, data interpretation, manipulation, & analysis of real-world sized data sets, and developing methods and practices that aid in scientific communication using the R statistical analysis environment.
Canvas will be used as the official grade repository as well as where I post announcements about course content. - Communication with be through your @vcu.edu email address.
GIF with a hard G
Common Workflow:
Student Learning Objectives (SLO) are statements that directly specify what participants will know, be able to do, or be able to demonstrate when they have completed or participated in the specific learning activity.
These define the active verbs of your learning experiences. At the end of a course, you should be able to implement or demonstrate mastery from each SLO.
These all map onto MS/MEnvs Program-Learning Outcomes.
| Deliverable | Details | SLO |
|---|---|---|
| Welcome & Logistics | Setting up the computational environment for the class. | NA |
| Git, Github & Markdown | Establish a functional working knowledge of git, github, and begin learning Markdown | 2 |
| Data Types & Containers | Understanding the fundamental grammar and objects in R. | 1,2 |
| Tidyverse | Data manipulation. Like a boss. | 1, 2 |
| Graphics that DON’T suck | Hello publication quality graphics, using the grammar of graphics approach | 2,3 |
| AI & Data Analytics | Leveraging large language models to aid in scientific communication | 1,2,3,4 |
| Deliverable | Details | SLO |
|---|---|---|
| Statistical Confidence | Base understanding of statistical inferences and the properties of sampled data | 1,2,4 |
| Binomial Inferences | Analyses based upon counts and expectations. | 4 |
| Categorical~f(Categorical) | Contingency table and categorical count data | 4 |
| Continuous~f(Categorical) | Analysis of Variance (or equality of means) | 4 |
| Continuous~f(Continuous) | Correlation & Regression approaches | 4 |
| Categorical~f(Continuous) | Logistic regression | 4 |
| Deliverable | Details | SLO |
|---|---|---|
| Points, Lines, & Polygons | Spatial data in vector format vectors | 3,5 |
| Raster Data | Continuously distributed spatial data | 3,5 |
| Spatial Analyses | Performing spatially explicit analyses of point process and habitat. | 3,4 |
| Raytracing | Higher dimensional visualization of spatial extents. | 3,5 |
Each of the topics listed are entirely self-contained. They will each have:
These items are intended to provide everyone a background foundation understanding of the topic and should be gone through prior to class where we are working on this topic.
This content is intended for in-person activities. In addition to presentation content, these items may include activities for individual work, group project work, reflection activities, and other assessment metrics.
The ability of participants to understand, practice, and demonstrate mastery of a topic can be evaluated using both direct and indirect methods. These will come in a variety of formats.
While each self-contained learning module is self-contained and will take as long as it takes to master, there are some limitations to how work is distributed and evaluated in this course.
Data analytics are never…
All evaluatory content will be due 1 week (7 days) later.
The grade for this course is based upon the totality of the points gained for all assignments as well as a single large data analysis project that will be due at the end of the semester. Grades will be determined using the normal 10% scale:
The Office of the Provost has a list of additional information relevant for syllabi. Please go visit that page and look over it to familiarize yourself with University-wide regulations and guidelines.